In commonly assigned U.S. Pat. No. 5,134,573, Goodwin discloses a method for adjusting the tone scale for digitally scanned photographic film systems. This method improves the overall image contrast of the processed digital images through the application of a tone scale function designed to linearize the photographic response of conventional photographic film products. Presented by Goodwin in this patent is a mathematical formula for constructing a tone scale function which relies on several control parameters. The mathematical formula was designed to accommodate a generalized photographic film product. The control parameters must be set according to the film response characteristics for a given photographic film product to achieve optimal results. One of the control parameters discussed is sensitive to the level of noise present in the digital image. This is important to Goodwin's method due to the fact that calculated tone scale function has a slope that is greater than unity. Therefore, applying tone scale function will, in general, amplify the noise of the resultant processed digital image. Goodwin describes how to optimize this control parameter to minimize the adverse effects of the noise amplification.
In U.S. Pat. No. 4,974,017, Terashita discloses a printing exposure method which measures and utilizes a method for measuring the photographic response to light characteristics of color negative photographic film products. This method calculates, for a particular film, a gradient value for an underexposure portion and a gradient value of an overexposure portion of the film's exposure range. The method disclosed by Terashita is designed to work for optical printing devices and the method as disclosed cannot be used to directly enhance the appearance of digital images derived from color negative photographic film products.
In U.S. Pat. No. 4,816,863, Lee discloses a method of generating an exposure dependent look-up-table for electrophotographic systems. The look-up-table generation method disclosed uses a series of step wedges (uniform patches of reflective targets) to characterize the response of the electrophotographic system. This exposure dependent look-up-table is then used to modify the digital signal prior to exposing the electrophotographic film and linearizes the effective response of the electrophotographic system. However, the method disclosed by Lee also amplifies the noise in the digital signal corresponding to portions of the response range for which the look-up-table has a slope greater than one.
There are many methods design to remove noise from digital images, however, most methods make use of spatial filtering techniques. One class of noise reduction spatial filters employs a non-linear localized spatial filtering technique directly to a digital image. An example of such a noise reduction filter is the Sigma Filter, described by Jong Sen Lee in the journal article Digital Image Smoothing and the Sigma Filter, Computer Vision, Graphics, and Image Processing Vol. 24, p. 255-269, 1983. This noise reduction filter uses a non-linear pixel averaging technique sampled from a rectangular window about the center pixel. Pixels in a local neighborhood about the center pixel are either included or excluded from the numerical averaging process on the basis of the difference between the local pixel and the center pixel. The small local neighborhood of pixels used by the Sigma filter make it simple to implement and effective at removing the high spatial frequency components of noise.
Gaussian noise sources exhibit random noise fluctuations over a large range of spatial frequencies. Although the Sigma filter was designed to work with Gaussian noise sources, the Sigma filter generally only removes the highest spatial frequency components of noise and thus has difficulty removing the low spatial frequency components of noise. This is largely due to the fact that the Sigma Filter operates on a small local neighborhood of pixels. Therefore, lower spatial frequency components of noise are not removed. The resultant processed digital images with the Sigma filter can have a mottled appearance particularly for sky regions of images that have little image structure.
Multiresolution, or pyramid, methods as a means of representing images as a function of spatial resolution for image processing as a long history. Burt and Adelson, described a method of representing a digital image by a series of residual images and a base digital image in their journal article “The Laplacian Pyramid as a Compact Image Code” IEEE Transactions on Communications, Vol. Com-31, No. 4, April 1983. Although the method taught by Burt and Adelson was designed with image compression methods in mind, the spatial frequency representation has application for noise reduction filtering methods.
The wavelet spatial frequency decomposition method has also been employed for the use of removing noise from digital images. In U.S. Pat. No. 5,526,446, Adelson et al. disclose a technique which converts an image into a set of coefficients in a multi-scale image decomposition process followed by the modification of each coefficient based on its value and the value of coefficients of related orientation, position, or scale. While the method disclosed by Adelson et al. is capable of removing noise of low and high spatial frequency, their method must be applied to the set of multi-scale coefficients and cannot be directly applied to a digital image.
Noise in digital images is generally exhibited throughout a range of spatial frequencies. The Sigma filter can be used to remove only the highest spatial frequency components of noise. The methods disclosed by Wober et al. and Adelson et al. can be used to remove the low and high spatial frequency components of noise. However, the class of simple spatial noise filters, such as the Sigma filter, can be applied directly to a digital image while the spatial frequency pyramid-based methods must employ noise filters designed to work with residual images.
The above mentioned methods disclosed by Adelson et al, Wober et al, and Jong Sen Lee can be used to remove noise from digital images in the same system that employs the tone scale function disclosed by Goodwin, to enhance the appearance of digital images. However, independent of whether a noise reduction algorithm is applied before or after the application of Goodwin's tone scale function, some noise amplification will result from the application of Goodwin's tone scale function.
In U.S. Pat. No. 5,012,333, Lee et al. disclose a method for preserving the high frequency detail of an image when applying a tone scale function to a digital image. The method disclosed by Lee et al. includes transforming a color digital image into a luminance chrominance representation, blurring the image luminance component with a low-pass spatial filter, subtracting the low-pass signal from the image luminance channel to produce a high-pass signal. The tone scale function is then applied to the low-pass signal and the high-pass signal is added to this result to produce an enhanced digital image. The method disclosed by Lee et al. has been designed to work with tone scale functions constructed to reduce the overall scene dynamic range. As such, the method disclosed by Lee et al. was not designed to work with the contrast expanding types of tone scale functions produced with Goodwin in U.S. Pat. No. 5,134,573. Similarly, U.S. Pat. Nos. 5,454,044, 5,467,404, 5,805,721, and 5,907,642 each disclose a method of spatially filtering digital images for the purposes of applying a tone scale function for the purposes of reducing the effective dynamic range of high dynamic range digital images. As with the method disclosed by Lee et al., these methods are designed to work with contrast reducing tone scale functions.